A novel linear source model and a unified rate control algorithm for H.263/MPEG-2/MPEG-4
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Joint selection of source and channel rate for VBR video transmission under ATM policing constraints
IEEE Journal on Selected Areas in Communications
Price-based distributed algorithms for rate-reliability tradeoff in network utility maximization
IEEE Journal on Selected Areas in Communications
Bit-rate control using piecewise approximated rate-distortion characteristics
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Constant quality constrained rate allocation for FGS-coded video
IEEE Transactions on Circuits and Systems for Video Technology
Transmission Distortion Analysis for Real-Time Video Encoding and Streaming Over Wireless Networks
IEEE Transactions on Circuits and Systems for Video Technology
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We develop a relatively accurate and robust R-D control algorithm in the H.264/AVC to achieve the target bit rate. More specifically, we first present an efficient bandwidth resource allocation framework to obtain a relatively accurate bandwidth resource allocation as bit rate budget, which is based on a proposed multi-hop packet delay bound violation model to predict the packet loss probability. With this widely-used offline-training-online-estimation mechanism, the proposed model is potentially applicable to almost all network conditions and can provide fairly accurate estimation results as compared with other models with a given sample data set. Once the channel bandwidth has been allocated to each video program and the target bit rate for each video frame is determined, and then, we introduce the concept of low-pass filtering of R-D functions and develop a smoothed rate control (SRC) framework for video recording and streaming. The extensive simulations demonstrate the SRC algorithm is effective and the proposed SRC algorithm has very low computational complexity and implementation cost.